{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Example 1: Infiltration of Water into a Single-Layered Soil Profile "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook presents steps to replicate example 1 from: *David Rassam, Jirka Šimůnek, Dirk Mallants,and Martinus Th. van Genuchten, The HYDRUS-1D Software Package for Simulating the One-Dimensional Movement of Water, Heat, and Multiple Solutes in Variably-Saturated Media* \\\n",
    "Tutorial \\\n",
    "Version 1.00, July 2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example provides insctructions to create a Pydrus model that involves infiltration of water from a ponded soil surface into a 1-m deep single-layered loam soil. The soil profile is initially unsaturated, having an initial pressure head of -100 cm. The upper boundary and lower boundary are represented with: \n",
    "\n",
    "* Upper BC: Constant Pressure Head (pressure head at the soil surface is 1 cm)(corresponding to the ponding depth).\n",
    "* Bottom BC: Free Drainage boundary condition (groundwater table is at an unspecified point deep in the profile (e.g., more than 10 m). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Import the Pydrus package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pydrus as ps\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Create the basic model & add time information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Directory example_1 created\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.08333333, 0.16666667, 0.25      , 0.33333333, 0.41666667,\n",
       "       0.5       , 0.58333333, 0.66666667, 0.75      , 0.83333333,\n",
       "       0.91666667, 1.        ])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Folder for Hydrus files to be stored\n",
    "ws = \"example_1\"\n",
    "exe = os.path.join(os.getcwd(),\"../../source/hydrus.exe\")  \n",
    "# Description\n",
    "desc = \"Infiltration of Water into a Single-Layered Soil Profile\"\n",
    "# Create model\n",
    "ml = ps.Model(exe_name=exe, ws_name=ws, name=\"model\", description=desc, mass_units=\"mmol\",\n",
    "              time_unit=\"days\", length_unit=\"cm\")\n",
    "ml.basic_info[\"lFlux\"] = True\n",
    "ml.basic_info[\"lShort\"] = False\n",
    "\n",
    "ml.add_time_info(tmax=1, print_times=True, nsteps=12, dt=0.001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Add processes and materials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"6\" halign=\"left\">water</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>thr</th>\n",
       "      <th>ths</th>\n",
       "      <th>Alfa</th>\n",
       "      <th>n</th>\n",
       "      <th>Ks</th>\n",
       "      <th>l</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.078</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.036</td>\n",
       "      <td>1.56</td>\n",
       "      <td>24.96</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   water                               \n",
       "     thr   ths   Alfa     n     Ks    l\n",
       "1  0.078  0.43  0.036  1.56  24.96  0.5"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.add_waterflow(top_bc=0, bot_bc=4)\n",
    "\n",
    "m = ml.get_empty_material_df(n=1)\n",
    "m.loc[[1, 1]] = [[0.078, 0.43, 0.036, 1.56, 24.96, 0.5]]\n",
    "ml.add_material(m)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Add profile information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "elements = 100  # Disctretize soil column into n elements\n",
    "depth = -100  # Depth of the soil column\n",
    "ihead = -100  # Determine initial Pressure Head\n",
    "# Create Profile\n",
    "profile = ps.create_profile(bot=depth, dx=abs(depth / elements), h=ihead)\n",
    "profile.iloc[0, 1] = 1  # Define initial Pressure Head at the surface\n",
    "ml.add_profile(profile)  # Add the profile"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5 Add observation nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add observation nodes at depth\n",
    "ml.add_obs_nodes([-20, -40, -60, -80, -100])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Write hydrus input files & run hydrus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CompletedProcess(args=['C:\\\\Matevz_arbeit\\\\pydrus\\\\examples\\\\hydrus_orig\\\\../../source/hydrus.exe', 'example_1', '-1'], returncode=0)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ml.write_input()\n",
    "ml.simulate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7 Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268e7e81c50>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.plots.obs_points()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268eaf275f8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.plots.profile_information()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x268eb0cf940>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.plots.profile_information(\"Water Content\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x00000268EB1EA8D0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000268EB1FE3C8>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.plots.water_flow(data=\"Actual Surface Flux\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x00000268EB281470>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000268EB296DD8>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W9t0D3NPRc5v0cqiLzxLUEVT1VRHpDYxyX3pbVa27z3jqjON60a1zLjOrN2V8gnoVaLyr/TVgQtQ+BTqcoEz2sC6+2ETkCuAXwAKcHooHReRmVX3K18BMoOWGQ4wd3ocXlm1i38F68nPDfofUqtZG8X07lYGYYLJBEi26DRjV2GoSkSJgHmAJyniqKlLC9Hc+ZsGarYwdUex3OK3KjMHwJmNZC6pFoSZdejuw+mhS4NRBPeh5VCdeqK71O5Q2dWSyWGPiZoMkWjRXRF4EHnefTwTm+BiPyRI54RDjyvrw9JJP2HOgjs6d0jcN2F9sxlPhkCWoWFT1Zpy5KCNAOTBVVW/xNyqTLaoiJew9WM8rq9N7XE6bqVNEwsB4mi8L8IB3YZmgsAQVm4j8m6reStRgo6jXEj33TTgDMIpUdXui5zPBM6q0B0WFecys3sSE8hK/w2lRPC2oF4CrgJ5AYdRmTJtskESLLojx2rhETyoix7rn/ijRc5ngCoeE8WXFzF+zjS/3J7o4hXfi6Xzsp6oRzyMxgdQ4SKLBWlAAiMh1wPXAYBGpjtpVCLyRhCJ+DdwCtDTfpTGAc9PutDc2Mm/VFv7hpJhzcvsunhbUHBG50PNITCA1DpKoswTV6M849xQ+5/7buJ2sqt9I5MQicjHwiaouSzhKE3gj+3enuGs+M6vTd7rTeFpQi4BnRCQEHMS5qVBVtYunkZlAsGtQR1LVL4AvRKROVT+M3icif1LVb7b2/tZWCQB+AsT1x6QtV2NCIeGismL++OZGvth7kK4FuX6H1Ew8LahfAacBnVW1i6oWWnIy8bIE1aLh0U9EJAc4ua03qer5qjqi6QZ8AAwElonIRpyVAN4VkVjJDFWdqqqVqlpZVJQ5C9iZ5KqKFHOwXnl51Ra/Q4kpngT1d2CFql3lNu1ngySOJCJTRGQXEBGRnSKyy32+hQSuG6nqclU9RlVLVbUUZ+makaq6OTmRmyCqOLYb/boXpG03XzxdfLXAAhGZw5FLU9swc9MmGyRxJFW9F7hXRO5V1Sl+x2Oym4gwPlLMf/9tA5/tPkD3ozr5HdIR4mlBbQBeATphw8xNO9kgidhUdYqIXCwiv3S3qiSfv9TugTLxmBApoa5BeXFl+jW222xBqerPAESk0HmqX3oelQmMcNhJUNaCOpKI3AuMBh5zX7pBRM6wVpVJteElXRjQszMzq2uZNDq9Bsy02YISkREi8h6wAlgpIktEZHhb7zMGrAXVivHABar6B1X9AzDWfc2YlBIRqiLFvLF+Ozu+3N/2G1Ioni6+qcCPVHWAqg4A/i/wX96GZYIiZIMkWtMt6nFX36IwWa8qUkKDwpwV6dXNF0+COkpV5zc+UdUFwFGeRWQCJccGSbTkXuA9EZkmIo8CS4Cf+xyTyVJD+xQyuOiotBvNF0+C+kBE7hCRUne7HWfghDFtsi6+2FT1ceBUnMliZwCnqep0f6My2coZzVfCWxs+ZevOfX6Hc0g8CepqoIjDFakXzuSxxrTJBkk0JyI5IiKqWgu8B+QC6b20qQm8CZFiVGH28vRZyDCeBHW+qv5AVUe62w+JPRNz3ETkayKyUkQaRKQy6vVSEdkrIkvd7T8TKcf4z1pQRxKR7wJbgQ/dx68AlwPTRSThpTaM6aghvQs5oXchszIsQcUa9proUNgVwGXAazH2rVfVCne7NsFyjM8aB0k02CCJRj8EBgNfAf4dOF1VJwEnAd/yMzBjqiLFvLPxM2q/2Ot3KEArCUpExonIg0BfEflt1DYNSGgBEVVdraprEjmHyQyNgyTq6i1BuQ6o6meq+hGwrvFmWlXdAxzwNzST7cZHnJ7mWdXp0YpqrQW1CVgM7MMZYdS4PQ+M8TCmgSLynoi8KiJntnSQiFwjIotFZPG2bds8DMckwp0r1lpQhxWIyEkicjLQyX080n2e73dwJrsNKjqaYcVdmJkmCarFmSTcNWWWiUhvVX00ep+I3AD8prUTt7YsgKq2NClmLdBfVXe4FfZZERmuqjtjxDcV5x4tKisr7bdfmhIRRMDmGj6kFmicx3Jz1OPG58b4qqq8mPvnruHjT/dwbI/OvsYSz2Sxk4D7m7x2FW0kKFU9v73BqOp+3AlpVXWJiKwHjsdpyZkMFRKxG3VdqnqO3zEY05qqshLun7uG2ctr+eezB/saS2vXoL4uIi/gdLk9H7XNB3Z4EYyIFIlI2H08CBiCs86NyWBhEeob/I7CGBOP/j07U96va1p087XWgnoDpzuiF86ihY12AdWJFCoilwIP4txfNUtElqrqGOAs4G4RqQPqgWtV9dNEyjL+C4Wsi8+YTFIVKeGe2avZuH03pb38mzioxRaUqn6oqgtU9TTgfQ4vs1GjqomO4ntGVfupap6q9naTE6r6tKoOV9Vy956rFxIpx6SHkIitqGtMBrmocTSfz/dExTOb+deAt4GvAVcAb4nI5V4HZoIjLILlJ2MyR99uBYzs3833br54btS9HRilqpNV9Vs4a9jc4W1YJkhEbJh5PETkXb9jMKZRVaSE1bU7Wb/NvyUA40lQIVXdGvV8R5zvMwaAcMi6+OKhqiP9jsGYRheVFSMCM5f514qKJ9HMFZEXReQqEbkKmAXM9jYsEyThkFgLqgUi0kVEejRufsdjTKM+XfMZNaCHr0twtJmgVPVm4HdABCgHpqqqTWpp4iZiCaopEflnEdmCMyK2cZYWu9/PpJWq8mL+vvVL1mze5Uv5cXXVqeoMVf0RcA/wrLchmaAJi9Bg90E1dRMwXFVLVXWguw3yOyhjoo0bUUxIYJZPrajWbtQ9VUQWiMgMd76wFTizkG8RkbGpC9FkupDYku8xrAf2+B2EMa0pKszj1EE9mVld68u9jK3dqPsQ8BOgK/BXYJyqLhKRocDjwNwUxGcCIGTXoGKZArwhIm/hTu8FoKo/8C8kY5qripTwk2eWs6p2J8NLuqa07Na6+HJU9SVV/QuwWVUXAajq+6kJzQRFSMRW1G3udzh/+C3iyNUCjEkrY0f0IRwSX+6Jaq0FFX3VoOnqVfbbxsQtHBJsOahm6tzrusaktR5HdeL0wT2ZWb2JW8acgLirZKdCay2ochHZKSK7gIj7uPF5WYriMwEQsht1Y5nvrmlWbMPMTbqbECnh40/3svyTL1Jabmtz8YVVtYuqFqpqjvu48XluKoM0mc26+GL6P7jXobBh5ibNjRneh9xw6rv5bEYI4zm7Ube5qKHlA22YuUl3XTvn8pXjejErxaP5LEEZz4mtB9WMiHwr1paE835fRNaIyEoRabrQqDEdVhUp4ZPP9/LuR5+nrMx4VtQ1JiHhkF2DimFU1ON84DzgXeCPHT2hiJwDXAJEVHW/iByTWIjGHHbB8N50mhFiVnUtJw/onpIyLUEZz4VtqqNmVPX70c9FpCvwpwRPex1wn6rud8vY2sbxxsStS34uZ59QxOzltdw+/kRCIe9H81kXn/Gc2IKF8dgDDEnwHMcDZ4rIWyLyqoiMaulAdwThYhFZvG3btgSLNdmiKlLM5p37WPzhZykpz1pQxnPhkGANqCOJyAscvp8wBAwDnozjffOAPjF23YZTn7sDp+J0IT4pIoM0xlVtVZ0KTAWorKy0/x0Tl/NO7E1eToiZ1ZsYPdD7uyIsQRnPhQRrQTX3y6jHdcCHqlrT1ptU9fyW9onIdcAMNyG9LSINQC/AmkgmKY7Oy+Hcoccwe/lm7pwwnLDH3XzWxWc8F7JrUIeIyHEicoaqvhq1vQ4MFJHBCZ7+WeBct5zjgU7A9gTPacwRqiIlbP9yP29t2OF5Wb4kKBH5hYi8LyLVIvKMiHSL2jdFRNa5Q2XH+BGfSS5LUEf4dyDW4jp73X2J+AMwyF15YDowOVb3njGJOHfoMRTkhlNy065fLaiXgRGqGgHW4txRj4gMAyYBw4GxwH+ISNinGE2S2JLvRyhV1eqmL6rqYqA0kROr6gFVvVJVR6jqSFX9ayLnMyaWgk5hzjvxGOau2Eydxzc4+pKg3FnS69yni4B+7uNLgOmqul9VNwDrgNF+xGiSx1luw+8o0kZ+K/sKUhaFMQmoipTw6e4DvLHe226+dLgGdTUwx33cF/g4al+N+5rJYDZZ7BHeEZHvNn1RRL6DLbdhMsRXTyji6LwcZnq80q5no/haGw6rqs+5x9yGM4Lpsca3xTg+5m82EbkGuAagf//+CcdrvGM36h7hh8AzIvINDiekSpwBDZf6FpUx7ZCfG+aCYb15ceUW/t8/NNApx5u2jmcJqrXhsAAiMhmoAs6LupBbAxwbdVg/IGaKtvs4MofNxXeYqm4BTnenJRrhvjzLrheZTFMVKeaZ9z7h9XXbOWeoN7Nq+XIflIiMBW4FzlbVPVG7ngf+LCIPACU4d9a/7UOIJonCIWy5jSZUdT4w3+84jOmorwzpRWF+Di9UbwpWggIeAvKAl93VGRep6rWqulJEngRW4XT9/Yuq1vsUo0kSW27DmODJywkzZngfXlyxmX0H68nPTf6Aa79G8R2nqseqaoW7XRu17x5VHayqJ6jqnNbOYzKDiFBvCcqYwKmKFLNrfx2vrfVmspJ0GMVnAi4sNhefMUF0xnG96N45l1nLvblp1xKU8ZzNxWdMMOWGQ4wd0Yd5q7aw72Dyr8ZYgjKeC9k1KGMCa3xZCbsP1DP//eQvP2YJynguJGKj+IwJqFMH9aDnUZ08mZvPEpTxXNgGSRgTWDnhEOPK+vDK+1vYc6Cu7Te0gyUo4zmbi8+YYKuKlLDvYAOvrE5uN58lKOO5kNiNusYE2ajSHhQV5iV9bj5LUMZzdqOuMcEWDgnjy4qZv2Ybu/YdTNp5A7vk+8GDB6mpqWHfvn1+h+Kp/Px8+vXrR25urt+htCgkth5UpsmW+pNqmVBfO6oqUsy0NzYyb/UWLj2pX9tviENgE1RNTQ2FhYWUlpbiTqcUOKrKjh07qKmpYeDAgX6H0yJnRV2/ozDtkQ31J9Uypb521Mj+3Snums+s6tqkJajAdvHt27ePnj17BrpyiQg9e/ZM+79ywyFbDyrTZEP9SbVMqa8dFXK7+V5du40v9ianmy+wCQrIisqVCZ/RuvgyUyb8bGWaoH+n4yPFHKxXXlq5OSnnC3SCMukhFLK5+IzJBhXHdqNf94Kk3bRrCSrFNm7cSEFBARUVFZSXl3P66aezZs2aVt+zdOlSZs+enaIIky8k2I26Ji1cfvnlfPDBB60eEw6HD9XPkSNH8sYbb7R6/MaNG/nzn/986Pny5cu56qqrkhFuxhERxkeKeX3ddj7bfSDh81mC8sHgwYNZunQpy5YtY/Lkyfz85z9v9fhMT1C25LtJBytXrqS+vp5Bgwa1elxBQcGh+nnvvfcyZcqUVo9vmqDKysqoqanho48+SkrcmWZCpIS6BuXFJHTzBXYUX7SfvbCSVZt2JvWcw0q6cOeE4a0ec+uttzJgwACuv/56AO666y4KCwuPOGbnzp10794dcC5MX3fddSxevJicnBweeOABzjjjDH7605+yd+9eFi5cyJQpU5g4cWJSP4vXxF1uQ1UD3wcfRH7Un927d3PFFVdQU1NDfX09d9xxB7feeiuLFy+mV69eLF68mJtuuokFCxZw1113sWHDBmpra1m7di0PPPAAixYtYs6cOfTt25cXXniB3NxcHnvsMS655BIAHnnkETZs2MD9998PwLRp01iyZAkPPvjgEXFE109V5ZZbbmHOnDmICLfffjsTJ07kxz/+MatXr6aiooLJkydz4403MmHCBKZPn84tt9yS1O8tEwwv6cKAnp2ZWV3LpNH9EzqXtaA8NGnSJJ544olDz5988klGjRrF+vXrqaioYPDgwTzwwAP86Ec/AuDhhx8GnC6Cxx9/nMmTJ9PQ0MDdd9/NxIkTWbp0acYlJ3Bu4gNbcsPEb+7cuZSUlLBs2TJWrFjB2LFjWz1+/fr1zJo1i+eee44rr7ySc845h+XLl1NQUMCsWbMAeP311zn55JMBp6tvxowZh97/xBNPHKpbe/fupaKigqFDh/JP//RP3HHHHQDMmDHjUMtq3rx53HzzzdTW1nLfffdx5plnsnTpUm688UYAKisr+dvf/pb07yUTiAhVkWLeWL+d7V/uT+gNoe2wAAAMu0lEQVRcWdGCaqul45WTTjqJrVu3smnTJrZt20b37t3p37//oS4+cCrGNddcw9y5c1m4cCHf//73ARg6dCgDBgxg7dq1vsSeTI0JyvJTZvKj/pSVlXHTTTdx6623UlVVxZlnntnq8ePGjSM3N5eysjLq6+sPJbSysjI2btwIQG1tLUVFRQAUFRUxaNAgFi1axJAhQ1izZg1nnHEGcLiLD+DNN9/kW9/6FitWrGDhwoV8/etfJxwO07t3b84++2zeeecdunTp0iyeY445hk2bkjvtTyapipTw8Pz1zFmxmW+eOqDD58mKBOWnyy+/nKeeeorNmzczadKkZvsvvvhivv3tbwNOF0IQNfbq2XUoE6/jjz+eJUuWMHv2bKZMmcKFF15ITk4ODQ0NAM3uJcrLywMgFAqRm5t7qCs5FApRV+fMsF1QUHDE+yZOnMiTTz7J0KFDufTSS2N2P5922mls376dbdu2tat+7tu3j4KCgvZ96AAZ2qeQwUVHMXPZpoQSlHXxeWzSpElMnz6dp556issvv7zZ/oULFzJ48GAAzjrrLB577DEA1q5dy0cffcQJJ5xAYWEhu3btSmncyRSWxhaUJSgTn02bNtG5c2euvPJKbrrpJt59911KS0tZsmQJAE8//XS7z3niiSeybt26Q88vu+wynn32WR5//PEWu87ff/996uvr6dmzJ2eddRZPPPEE9fX1bNu2jddee43Ro0fHrJ9r165lxIgR7Y4xKJxuvhLe3vgpW3d2/MZkS1AeGz58OLt27aJv374UFxcDHLoGVV5ezk9+8hN+//vfA3D99ddTX19PWVkZEydOZNq0aeTl5XHOOeewatUqKioqjrimlSlCYtegUkFEKkRkkYgsFZHFIjLa75g6avny5YwePZqKigruuecebr/9du68805uuOEGzjzzTMLhcLvPOX78eBYsWHDoeffu3Rk2bBgffvgho0cf/qoar0FVVFQwceJEHn30UcLhMJdeeimRSITy8nLOPfdc7r//fvr06UMkEiEnJ4fy8nJ+/etfAzB//nzGjx+f8PeQyaoixajC7OUJ3BOlqinfgF8A7wPVwDNAN/f1UmAvsNTd/jOe85188sna1KpVq5q9FlTp/ll//7cPdMCtM/XzPQf8DqXDgMXqQ11pzwa8BIxzH18ELIjnfdlSf/bs2aOnnHKK1tXVeVrOvn379JRTTtGDBw/G3B/E77YlFz7wqv7jf7ze4frj1zWol4EpqlonIv8GTAFudfetV9UKn+IyHgi7XftLP/6cwny77OkhBRqv2HcFsvcqfQwFBQX87Gc/45NPPqF//8SGP7fmo48+4r777iMnx37WqyLF/Orljg/08uUbVNWXop4uAppfnDGBcVSe82M2+Q9v+xxJ4P0QeFFEfonTfX96SweKyDXANYCnv6zTzZgxYzwvY8iQIQwZMsTzcjLB+ExMUE1cDURfWBkoIu8BO4HbVTXmzQTxVDDNghtDNQMGHlxS0Ze+3Qo4UN/gdygdds6/+R2BQ0TmAX1i7LoNOA+4UVWfFpErgP8Gzo91HlWdCkwFqKysjPlDlA31J9Uyob4m06Cio3nkGyO5qIP1x7ME1VpFUtXn3GNuA+qAx9x9tUB/Vd0hIicDz4rIcFVtdht7WxUsPz+fHTt2BHrJAHXXl8nPz/c7lFZ1yglx+nG9/A4jEFQ1ZsIBEJE/Aje4T/8C/L6j5WRD/Um1TKmvyTaurLjD7/UsQbVWkQBEZDJQBZyn7p8Vqrof2O8+XiIi64HjgcXtLb9fv37U1NSwbdu2dseeSRpX6DQG55rT2cAC4Fzg7x09UbbUn1Sz+to+vnTxichYnEERZ6vqnqjXi4BPVbVeRAYBQ4DWpx5uQW5ubiBXrTSmFd8FfiMiOcA+3C7wjrD6Y9KBX9egHgLygJfd7oNFqnotcBZwt4jUAfXAtar6qU8xGpNRVHUhcLLfcRiTLH6N4juuhdefBtp/i7gxxpjAsZkkjDHGpCUJwrBHEdkFtL4srXd6AduzqNxsLfsEVS1s+7DM42P9ycafo2z8zNDB+pMO90ElwxpVrfSjYBFZ7EfZfpWbrWWLSLtHkmYQX+pPtv4cZdtnbiy7I++zLj5jjDFpyRKUMcaYtBSUBDU1C8vOxs/sZ9l+fmavZeN3ap85A8oOxCAJY4wxwROUFpQxxpiAsQRljDEmLWVUghKRsSKyRkTWiciPY+zPE5En3P1viUhpisr9kYisEpFqEXlFRAYko9x4yo467nIRURFJ2jDSeMoWkSvcz75SRP6cinJFpL+IzBeR99zv/KIklfsHEdkqIita2C8i8ls3rmoRGZmMclPBr7oTZ9mBqz9+1Z14ys6o+tORZXj92IAwsB4YBHQClgHDmhxzPe4y8cAk4IkUlXsO0Nl9fF0yyo23bPe4QuA1nMUfK1P4fQ8B3gO6u8+PSVG5U4Hr3MfDgI1J+sxnASOBFS3svwiYAwhwKvBWsn/Ovdj8qjvtKDtQ9cevutOOsjOm/mRSC2o0sE5VP1DVA8B04JImx1wCPOo+fgo4TxJfzKbNclV1vh6elX0RkKz59OP5zAD/CtyPM4N1ssRT9neBh1X1MwBV3Zqicj1Z2lxVXwNam5z4EuCP6lgEdBORji92kzp+1Z24yg5g/fGr7sRbdsbUn0xKUH2Bj6Oe17ivxTxGVeuAL4CeKSg32ndw/kpIhjbLFpGTgGNVdWaSyoy7bJy1uo4XkddFZJE4y6ikoty7gCtFpAaYDXw/CeXGo70/C+nCr7oTb9nRglB//Ko78ZZ9FxlSfzJpqqNYf801HSMfzzFelOscKHIlUImzaFwytFq2iISAXwNXJam8uMt25eB0VXwV56/ev4nICFX93ONyvw5MU9VfichpwJ/ccr1eU96Ln69U8KvutOu8Aao/ftWdeMvOmPqTSS2oGuDYqOf9aN40PXSMOIu2daX1JmeyykVEzgduAy5WZ2XgZGir7EJgBLBARDbi9Os+n6QLvfF+38+p6kFV3YAz4eiQFJT7HeBJAFV9E8jHmQjTa3H9LKQhv+pOvGUHrf74VXfiLTtz6k8yLo6lYsP5i+MDYCCHL/4Nb3LMv3Dkhd4nU1TuSTgXJoek+jM3OX4ByRskEc/nHgs86j7uhdN875mCcucAV7mPT3R/yCVJn7uUli/yjufIi7xvJ/P/26vNr7rTjrIDVX/8qjvtKDtj6o+nFSPZG84okLXuD/Nt7mt34/zVBc5fAn8B1gFvA4NSVO48YAuw1N2eT9VnbnJsUipYOz63AA8Aq4DlwKQUlTsMeN2tfEuBC5NU7uNALXAQ56+97wDX4qzs3Ph5H3bjWp7M79rrza+6E2fZgas/ftWdOMvOmPpjUx0ZY4xJS5l0DcoYY0wWsQRljDEmLVmCMsYYk5YsQRljjElLlqCMMcakJUtQPhORniKy1N02i8gnUc/f8KC8q0Rkm4j8voX9C5I8I/ov3M91U7LOaQxY3ckGmTTVUSCp6g6gAkBE7gK+VNVfelzsE6r6PY/LAEBVbxaR3akoy2QXqzvBZy2oNCYiX7r/flVEXhWRJ0VkrYjcJyLfEJG3RWS5iAx2jysSkadF5B13OyOOMgpEZLq7PssTQEHUvkdEZLG7Xs3P3NfOE5Fnoo65QERmiEhYRKaJyAo3phuT/oUYEyerO8FgLajMUY4zLcmnOFOZ/F5VR4vIDTizEf8Q+A3wa1VdKCL9gRfd97TmOmCPqkZEJAK8G7XvNlX9VETCwCvu/r8CD4tIkapuA74N/A/OX7J9VXUEgIh0S9LnNiZRVncylLWgMsc7qlqrzkSa64GX3NeX48x/BXA+8JCILAWeB7qISGEb5z0L+F8AVa0GqqP2XSEi7+IsrDYcZ+EzBf6EM11/N+A0nPm1PgAGiciD4iwdsDOhT2tM8ljdyVDWgsoc0TM8N0Q9b+Dw/2MIOE1V97bz3M3muxKRgcBNwChV/UxEpuHM1wbOX30v4Czw9hd11g/6TETKgTE4E49eAVzdzjiM8YLVnQxlLahgeQk4dAFXRCrieM9rwDfc40cAEff1LsBu4AsR6Q2Ma3yDqm7CmQH5dmCa+95eQEhVnwbuwFn62ZhMYXUnDVkLKlh+gNPHXY3zf/sazmzCrXkE+B/3PUtxZrJGVZeJyHvASpwuiNebvO8xoEhVV7nP+7rnafyjZ0qiH8aYFLK6k4ZsNvMsIyJX4Uxzn9BQWRF5CHhPVf87jmPvIjVDgI3xjNWd1LMuvuyzFxgnLdxsGA8RWYLTnfG/cRz7C+BKnC4PYzKZ1Z0UsxaUMcaYtGQtKGOMMWnJEpQxxpi0ZAnKGGNMWrIEZYwxJi1ZgjLGGJOW/j8+rWV8F3dD9AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.plots.water_flow(data=\"Bottom Flux\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x00000268EB31B978>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x00000268EB35C780>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ml.plots.soil_properties()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
